Data Query Method of Science and Technology Management Based on Relational Engine

Nowadays, many applications require to represent both data and knowledge together. Regardless of the requirement, the normal practice is either using only database or only ontology. These approaches are proven to have drawbacks. There is still no proper methodology to obtain the benefits from both ontology and database management systems together. The issues raised by the rapprochements between such worlds are well known and addressed by consolidated mapping and transforming languages which may result in loss of data or hindering the reasoning power in knowledge. Nevertheless, to the best of our knowledge, a best practice for establishing such rapprochement is missing which we present through this research; a guideline based cooperation between database and ontology. We have introduced a hybrid approach of relational database and ontology for optimal management and querying of data and knowledge. Our method optimizes the storing mechanism by allowing of distributing data between ontology and database. Further, it optimizes the query execution by allowing some part of the data to be queried through database via SQL and some part of the data to be queried through ontology via SPARQL. We were able to obtain promising results about the performance in terms of storage and query execution and accuracy from the proposed approach.

Soft science and technology projects are one of the basic organizational forms of scientific and technological activities, being the process of generating, accumulating, and applying new knowledge.The evaluation of science and technology project is a key stage in the process of science and technology project management. In the new development stage, it is urgent to speed up the formation of a comprehensive innovative management system. Constructing an acceptance evaluation indicator system of soft science and technology projects is an important link to deepen the management of science and technology projects. Based on the OKR (Objectives and Key Results) method and combined with the characteristics of soft science projects, the acceptance evaluation objectives such as schedule, quality, cost, and team management are proposed. The evaluation objectives are transformed into quantifiable key outcome indicators, and a system of evaluation indicators for soft science and technology project acceptance in tobacco industry is constructed. Field research is carried out in the tobacco industry, using the entropy value method to assign weights to the evaluation indicators, with a view to providing a decision-making basis for the acceptance evaluation of the scientific and technological project management departments, and accelerating the formation of new quality productivity in the tobacco industry.

Deco is a comprehensive system for answering declarative queries posed over stored relational data together with data obtained on-demand from the crowd. In this paper we describe Deco's cost-based query optimizer, building on Deco's data model, query language, and query execution engine presented earlier. Deco's objective in query optimization is to find the best query plan to answer a query, in terms of estimated monetary cost. Deco's query semantics and plan execution strategies require several fundamental changes to traditional query optimization. Novel techniques incorporated into Deco's query optimizer include a cost model distinguishing between "free" existing data versus paid new data, a cardinality estimation algorithm coping with changes to the database state during query execution, and a plan enumeration algorithm maximizing reuse of common subplans in a setting that makes reuse challenging. We experimentally evaluate Deco's query optimizer, focusing on the accuracy of cost estimation and the efficiency of plan enumeration.

In this paper we examine effective representations of knowledge for the purposes of management of engineering and technology. Specifically, given the immense volume of data available about scientific outputs, it is highly necessary to condense or abstract this information for management use. This paper considers the utility of such representations in the management of technology. We ask further whether a given representation accurately depicts the knowledge contained in the science and technology database. We argue that, in this regard, generative models are superior because they provide explicit hypotheses about the structuring of the data. The second is whether the representation is interpretable by management, and therefore directly actionable. We argue that the number of model parameters is an indirect measure of the degree of difficulty of using and interpreting the selected representation. Combining the two metrics suggests the use of Akaike's Information Criteria, a metric used for model selection purposes. The AIC is used to evaluate existing model representations used in tech mining, both positional and relational. After surveying the results, we recommend the use of a mixed representation. These more complex models appear to offer a more useful representation of science and technology datasets. Furthermore there are multiple promising but previously unexplored representations of the data. The ramifications of further exploration within this range of possible new models is discussed.

The rise of the digital economy is shaping the future of business and society. In this digital era, data collection and analysis have become crucial to support decision-making, innovation, and efficiency improvement. Cloud computing and the Internet of Things technology, as the core driving forces of the digital economy, are opening new possibilities and providing strong support for data collection and analysis platforms. To broaden the scope of network big data applications, improve the accuracy of network data storage and management to a greater extent, and reduce the time for network data processing and control, a research method for network big data based on cloud computing and the Internet of Things in the context of the digital economy is proposed. The author first uses hierarchical network coding to transmit network data, based on the transmitted data, the CRC algorithm is used to calculate network data, and then the data is stored in a group storage manner, secondly, the high-precision query of network data is carried out using the hierarchical reverse stacking positioning method, thereby completing the research on network big data. Finally, to demonstrate the overall performance of network big data research methods based on cloud computing and the Internet of Things, a simulation experiment is conducted. The experimental results show that when the data query parameter ε is 8, the data query accuracy curve is unstable, and the query accuracy is low. When the data query parameter ε is 4-5, the data query accuracy curve is relatively flat, and the query accuracy is high. The method proposed by the author can comprehensively and concretely study network big data, improve data processing accuracy and network data calculation speed, increase network data storage capacity and query efficiency, reduce the loss rate of network data during operation, expand the operating range of network data, and provide a strong basis for subsequent research on network big data.

How to evaluate the performances of an engineering project management and what’s the key factors were important and hot topics in theories of practice of projects management field. This paper analyzed engineering project management in view of teams, summarized factors of engineering project, performances of successful engineering project, and built an engineering project management evaluation model. Then analyzed the model with statistics and structural equation modeling, and extracted some key factors or performances for engineering project management.

While Telemedicine technology greatly increases the quality of medical care with less health care costs, it also raises critical challenges for data security and privacy in the course of data entry, data review, patient education, video conference, and electronic monitoring. To meet these various needs, a number of different of security strategies can be utilized. In this study, we aim to secure user-related Telemedicine data by storing and distributing them in an encrypted form, so that on the one hand the requirement on secure medical data management is satisfied, while on the other hand efficient processing, particularly the efficient querying of encrypted medical data can still be guaranteed. Given the increasing use of eXtensible Markup Language (XML) in describing and interchanging data between systems and users, querying over encrypted XML Telemedicine data becomes more and more important. In [3], we develop an effective hash-based approach to enable efficient querying over encrypted XML data. The basic idea is to augment encrypted XML data with encodings which characterize the topology and content of tree-structured XML data, and then filter out candidate XML data for decryption and query execution by examining XPath queries against these encodings. A generic framework is provided which consists of three phases, namely, “query preparation”, “query pre-processing” and “query execution”, for the proposed search strategy. This paper presents a comprehensive experimental evaluation of querying over encrypted XML-formated Telemedicine data.

The data connection of electric power marketing and distribution network is an integrated platform of operation and distribution data based on geographic information platform, which integrates the data of marketing business application system, production management information system and grid geospatial information service platform, and shares the whole face of production data and marketing customer data, so as to achieve the connection of operation and distribution data and business, and realize fault location and electric failure Scope analysis, line loss statistics, business expansion and installation, etc. Graph database has the technical advantages of high efficiency of topological data organization, low redundancy of topological data storage, strong expansibility of topological data model and high efficiency of topological analysis and calculation, which can meet the storage, query and retrieval requirements of large-scale operation and distribution data. After application verification, the results show that the graph database can provide data storage, query and retrieval technology scheme for the operation and distribution of power grid. With the business development of operation and distribution of power grid, there are performance bottlenecks in the existing data storage mode, which seriously restricts the deepening of application. It is necessary to introduce advanced computing technology to achieve breakthrough, and improve the data storage efficiency of the existing data platform The parallel and concurrent ability of data retrieval and query can meet the management requirements of geometric growth of power grid equipment, the analysis requirements of real-time data of power grid, and the concurrent access requirements of largescale growth of users.

Multi-route query processing and optimization

Global information systems involve a large number of information sources distributed over computer networks. The variety of information sources and disparity of interfaces makes the task of easily locating and efficiently accessing information over the network very cumbersome. We describe an architecture for global information systems that is especially tailored to address the challenges raised in such an environment, and distinguish our architecture from architectures of multidatabase and distributed database systems. Our architecture is based on presenting a conceptually unified view of the information space to a user, specifying rich descriptions of the contents of the information sources, and using these descriptions for optimizing queries posed in the unified view. The contributions of this paper include: (1) we identify aspects of site descriptions that are useful in query optimization; (2) we describe query optimization techniques that minimize the number of information sources accessed; and (3) we demonstrate the need for interleaving planning and query execution in such a system, and present an algorithm for this purpose.

"Being stuck in traffic doesn't have to be a way of life." This prologue comes from the Elevated Transportation Company (ETC) board's letter in the ETC Seattle Popular Monorail Plan, one of the largest public works projects ever proposed in the city of Seattle. Three years after this proposal, on November 8, 2005, the Seattle Monorail Project (SMP) was shut down by voters. This paper critically analyzes the SMP through the lens of stakeholder theory. This perspective provides valuable insights into the failure of the SMP. We theorize that SMP's failure might have been avoided had its leadership recognized the many stakeholders that had power over the plan and, more important, the dynamic changes in relationships among the stakeholders. Failure might also have been avoided by managing conflicts in stakeholders' expectations. Specifically, we use stakeholder theory to develop four propositions that are relevant in the context of large-scale technology projects: (a) Organizations are more likely to succeed when they have effective mechanisms for monitoring and evaluating interactions among stakeholders and changes in their positions in relation to their strategic innovation projects; (b) organizations are more likely to succeed when they trade off the conflicts in expectations and interests that stakeholders hold; (c) organizations are more likely to implement complex technology projects if they understand stakeholders' expectations and the interplay among stakeholders; (d) organizations are more likely to achieve their innovative projects when they define stakeholders in terms of their power over strategic objectives. The paper makes a contribution to both the research and the practice of major technological infrastructure projects, strategic innovations, and government technology management.

Based on the powerful open interface of WeChat enterprise account features, a site investigation system is proposed for management of hydraulic engineering projects. The implemented functions include integrated photographing, GPS based positioning and information submission from handheld mobile devices. By leveraging cloud computing and mobile internet technologies, all the site investigation information is collected by designers on the unified mobile terminal including smart phones, tablets, and laptops. The proposed system provides various services, such as batch uploading, geological position mapping, data query and data downloading of the investigated information of specific hydraulic engineering project. Moreover, high level functions are realized through high-precision location based service, synchronous saving, resource sharing and quick search. Our system outperforms and changes the traditional working mode of site investigation of hydraulic engineering projects, as well as improves the working efficiency and accuracy for data collection in the whole life of the project management.

In cloud computing environments parallel kNN queries for big data is an important issue. The k nearest neighbor queries (kNN queries), designed to find k nearest neighbors from a dataset S for every object in another dataset R, is a primitive operator widely adopted by many applications including knowledge discovery, data mining, and spatial databases. This chapter proposes a parallel method of kNN queries for big data using MapReduce programming model. Firstly, this chapter proposes an approximate algorithm that is based on mapping multi-dimensional data sets into two-dimensional data sets, and transforming kNN queries into a sequence of two-dimensional point searches. Then, in two-dimensional space this chapter proposes a partitioning method using Voronoi diagram, which incorporates the Voronoi diagram into R-tree. Furthermore, this chapter proposes an efficient algorithm for processing kNN queries based on R-tree using MapReduce programming model. Finally, this chapter presents the results of extensive experimental evaluations which indicate efficiency of the proposed approach.

With the further development of social economy and science and technology, society has brought new impetus and new challenges for the development of mechanical and electrical engineering. In order to safeguard the sound operation of mechanical and electrical engineering, we must take into account the management effect of project costs while strictly controlling the construction quality of electromechanical engineering projects. There have been some attempts to apply BIM technology to the cost management of electromechanical engineering projects. In this environment, this paper takes the cost management of mechanical and electrical engineering projects of BIM technology as the research object. First, it briefly introduces the theory and characteristics of BIM technology. On the basis of this, it points out the advantages of cost management in combination of electromechanical engineering and BIM technology. Finally, the actual application of this technology in the cost control of electromechanical engineering projects is the final point of view, in order to provide a reference for the project construction time, energy saving, project quality assurance and so on.

As an important content of engineering project management, information integrated management system is a public service platform of the whole system integrate management. In some way, the engineering project management is equal to project information management. Under the theory of information integration, this paper analysis the important role of the owner in the information integrated management, and puts forward the large-scale chemical engineering project information integrated management should take the owner as main integrator and controller. Then, This paper research on the basic conditions for chemical engineering project information integration, Based on this, it build up three layers information integrated management system of large-scale chemical engineering projects based on owner; and analysis and researched the construction and implemented methods of data storage layer 、data managed and shared layer、application layer. Providing a mind of constructive about information integrate management of chemical engineering projects.